Chemical Solutions Company - Introduction

Industrial Materials Leader

From fragmented R&D data to an AI-ready innovation foundation

Building the data, governance, and architecture capabilities required to scale AI, automation, and advanced analytics across global Research & Development.

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Together we have achieved

Results worth seeing

high-value AI use cases enabled

core transformation areas addressed

60–100

FTE efficiency potential identified

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About the company

A global industrial materials leader

A provider of chemical solutions serving customers worldwide across multiple industries, including automotive, electronics, industrial manufacturing, renewable energy, transportation, and consumer goods. To accelerate innovation and scale AI adoption within Research & Development, the organization required a stronger data foundation to support advanced analytics, automation, and AI-driven decision-making across business-critical processes.

Our approach

Establishing an AI-ready data foundation for R&D transformation

Data, technology & governance assessment

Conducted a comprehensive assessment across the R&D organization to identify barriers limiting AI adoption, including fragmented data landscapes, inconsistent governance structures, and disconnected collaboration processes.

Data foundation & architecture design

Defined the target data model, governance framework, and collaboration standards required to create a scalable foundation for advanced analytics and AI applications.

AI use case identification & prioritization

Evaluated and qualified high-value AI opportunities across R&D based on business value, feasibility, and implementation dependencies.

Implementation roadmap development

Created a detailed execution roadmap covering organizational capabilities, required resources, timelines, and governance structures to accelerate implementation.

Key deliverables

Building the capabilities required to scale innovation

AI-ready data strategy & roadmap

Developed a comprehensive data strategy and implementation roadmap defining the capabilities, resources, governance structures, and timelines required to scale AI adoption across the R&D organization.

Target data architecture & governance framework

Designed a unified data model and governance framework to improve data quality, accessibility, consistency, and cross-functional collaboration across critical R&D processes.

AI use case portfolio & prioritization

Identified and evaluated 31 high-value AI and automation opportunities, prioritizing initiatives based on business impact, feasibility, and implementation readiness.

Product property prediction

Leveraged historical formulation and testing data to predict product performance, reduce physical testing requirements, and accelerate experimentation and product development cycles.

Centralized R&D search engine

Enabled semantic search across experiments, formulations, products, projects, and regulatory information through a unified data model and connected systems landscape.

Standardized collaboration processes

Established cross-functional standards and ways of working to improve knowledge sharing, data consistency, and scalability across the global R&D organization.

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